This paper uses a nonlinear vector autoregression and a non-recursive identification strategy to show that an equal-sized uncertainty shock generates a larger contraction in real activity when growth is low (as in recessions) than when growth is high (as in expansions). An estimated New...

This paper quantifies the finance uncertainty multiplier (i.e., the magnifying effect of the real impact of uncertainty shocks due to financial frictions) by relying on two historical events related to the US economy, i.e., the large jump in financial uncertainty occurred in October 1987 (which...

We employ real-time data available to the US monetary policy makers to estimate a Taylor rule augmented with a measure of financial uncertainty over the period 1969-2008. We find evidence in favor of a systematic response to financial uncertainty over and above that to expected inflation, output...

We estimate a nonlinear VAR model to study the real effects of monetary policy shocks in regimes characterized by high vs. low macroeconomic uncertainty. We find unexpected monetary policy moves to exert a substantially milder impact in presence of high uncertainty. We then exploit the set of...

We estimate a novel measure of global financial uncertainty (GFU) with a dynamic factor framework that jointly models global, regional, and country-specific factors. We quantify the impact of GFU shocks on global output with a VAR analysis that achieves set-identification via a combination of...

We employ a mixed-frequency quantile regression approach to model the time-varying conditional distribution of the US real GDP growth rate. We show that monthly information on the US financial cycle improves the predictive power of an otherwise quarterly-only model. We combine selected quantiles...